Psychometric properties of the MOS (Medical Outcomes Study) Sleep Scale in patients with neuropathic pain
Bibliographic record
Abstract
OBJECTIVE: This study assessed the psychometric properties of the MOS Sleep Scale in neuropathic pain (NeP). METHODS: Psychometric properties were tested in patients with neuropathic pain enrolled in a prospective study exploring the effectiveness of gabapentin for 3 months. Participants also completed scales for pain intensity, anxiety, depression, disability, and health-related quality of life. Feasibility, reliability, validity and sensitivity to change were measured in this study. RESULTS: Six-hundred-three patients [58.4+/-14.4 years (65.1% female), mean+/-SD] with pain for 1.2+/-3.3 years were included. The MOS Sleep Scale was acceptable (items with missing data <10% and floor and ceiling effects <50% per item and <15% per domain) and reliable (Cronbach's alpha between 0.64 and 0.87, and test-retest intraclass correlation coefficients between 0.79 and 0.91, p<0.001 for all cases). After adjusting for confounders, the MOS Sleep Scale was able to distinguish between sex, present pain intensity, level of disability and presence of anxiety or depression. Correlations with other scales were moderate; rho-coefficients between -0.21 and 0.57 (p<0.01, all cases). The scale was sensitive to change after treatment with gabapentin; after adjusting, responders (50% reduction in baseline pain) showed a decrease in sleep problems index of -25.6+/-14.3 points vs. -12.1+14.5 points in nonresponders (F=80.5, df=1/398, p<0.0001). Score reduction in summary index and subscales correlated significantly with pain intensity reduction (Pearson r-coefficients between 0.428 and 0.116, p<0.01, all cases). CONCLUSIONS: The MOS Sleep Scale showed good psychometric properties and was sensitive to changes in patients with NeP of broad aetiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".